Abstract
Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.
Highlights
Biometric systems are based on the subject’s characteristics to allow his/her identification [1]
This paper presents an improved biometric system that uses a Support Vector Machine (SVM) algorithm for classification and identification of subject
Figure shows the classification error rate obtained for each test, and Figure shows the corresponding computational burden. This computational burden is calculated as the product of the number of support vectors, employed by the SVM for the classification, and their size
Summary
Biometric systems are based on the subject’s characteristics to allow his/her identification [1]. To characterize people and classify them for subsequent identification and validation Each of these systems requires the use of specific sensors to obtain the desired characteristics of the subject. In a second stage of the analysis, a new global error function was proposed by weighting the MSE error of each image proportionately to the information that it provides In this case, an EER value of 4% was obtained. This paper presents an improved biometric system that uses a SVM algorithm for classification and identification of subject. Since high dimensionality of acoustic profiles exponentially increases the computational burden of SVM classifiers, preprocessing and feature extraction techniques have been designed and implemented to improve the classifier performance This new system is based on the results obtained in previous studies [18].
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